segmentation.h
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00001 #ifndef SEGMENTATION_H
00002 #define SEGMENTATION_H
00003 
00004 #include "typedefs.h"
00005 
00006 #include <pcl/ModelCoefficients.h>
00007 #include <pcl/sample_consensus/method_types.h>
00008 #include <pcl/sample_consensus/model_types.h>
00009 #include <pcl/segmentation/sac_segmentation.h>
00010 #include <pcl/filters/extract_indices.h>
00011 #include <pcl/segmentation/extract_clusters.h>
00012 
00013 
00014 /* Use SACSegmentation to find the dominant plane in the scene
00015  * Inputs:
00016  *   input 
00017  *     The input point cloud
00018  *   max_iterations 
00019  *     The maximum number of RANSAC iterations to run
00020  *   distance_threshold 
00021  *     The inlier/outlier threshold.  Points within this distance
00022  *     from the hypothesized plane are scored as inliers.
00023  * Return: A pointer to the ModelCoefficients (i.e., the 4 coefficients of the plane, 
00024  *         represented in c0*x + c1*y + c2*z + c3 = 0 form)
00025  */
00026 pcl::ModelCoefficients::Ptr
00027 fitPlane (const PointCloudPtr & input, float distance_threshold, float max_iterations)
00028 {
00029   // Intialize the SACSegmentation object
00030   pcl::SACSegmentation<PointT> seg;
00031   seg.setOptimizeCoefficients (true);
00032   seg.setModelType (pcl::SACMODEL_PLANE);
00033   seg.setMethodType (pcl::SAC_RANSAC);
00034   seg.setDistanceThreshold (distance_threshold);
00035   seg.setMaxIterations (max_iterations);
00036 
00037   seg.setInputCloud (input);
00038   pcl::ModelCoefficients::Ptr coefficients (new pcl::ModelCoefficients ());
00039   pcl::PointIndices::Ptr inliers (new pcl::PointIndices ());
00040   seg.segment (*inliers, *coefficients);  
00041 
00042   return (coefficients);
00043 }
00044 
00045 /* Use SACSegmentation and an ExtractIndices filter to find the dominant plane and subtract it
00046  * Inputs:
00047  *   input 
00048  *     The input point cloud
00049  *   max_iterations 
00050  *     The maximum number of RANSAC iterations to run
00051  *   distance_threshold 
00052  *     The inlier/outlier threshold.  Points within this distance
00053  *     from the hypothesized plane are scored as inliers.
00054  * Return: A pointer to a new point cloud which contains only the non-plane points
00055  */
00056 PointCloudPtr
00057 findAndSubtractPlane (const PointCloudPtr & input, float distance_threshold, float max_iterations)
00058 {
00059   // Find the dominant plane
00060   pcl::SACSegmentation<PointT> seg;
00061   seg.setOptimizeCoefficients (false);
00062   seg.setModelType (pcl::SACMODEL_PLANE);
00063   seg.setMethodType (pcl::SAC_RANSAC);
00064   seg.setDistanceThreshold (distance_threshold);
00065   seg.setMaxIterations (max_iterations);
00066   seg.setInputCloud (input);
00067   pcl::ModelCoefficients::Ptr coefficients (new pcl::ModelCoefficients ());
00068   pcl::PointIndices::Ptr inliers (new pcl::PointIndices ());
00069   seg.segment (*inliers, *coefficients);  
00070 
00071   // Extract the inliers
00072   pcl::ExtractIndices<PointT> extract;
00073   extract.setInputCloud (input);
00074   extract.setIndices (inliers);
00075   extract.setNegative (true);
00076   PointCloudPtr output (new PointCloud);
00077   extract.filter (*output);
00078 
00079   return (output);
00080 }
00081 
00082 /* Use EuclidieanClusterExtraction to group a cloud into contiguous clusters
00083  * Inputs:
00084  *   input
00085  *     The input point cloud
00086  *   cluster_tolerance
00087  *     The maximum distance between neighboring points in a cluster
00088  *   min/max_cluster_size
00089  *     The minimum and maximum allowable cluster sizes
00090  * Return (by reference): a vector of PointIndices containing the points indices in each cluster
00091  */
00092 void
00093 clusterObjects (const PointCloudPtr & input, 
00094                 float cluster_tolerance, int min_cluster_size, int max_cluster_size,
00095                 std::vector<pcl::PointIndices> & cluster_indices_out)
00096 {  
00097   pcl::EuclideanClusterExtraction<PointT> ec;
00098   ec.setClusterTolerance (cluster_tolerance);
00099   ec.setMinClusterSize (min_cluster_size);
00100   ec.setMaxClusterSize (max_cluster_size);
00101 
00102   ec.setInputCloud (input);
00103   ec.extract (cluster_indices_out);
00104 }
00105 
00106 #endif


pcl
Author(s): Open Perception
autogenerated on Mon Oct 6 2014 03:17:56